Optimization of school network using location-allocation analysis: Case study: Bandung, Indonesia

Alifi, Muhammad Riza; Hayati, Hashri; Supangkat, Suhono Harso

doi:10.1109/tenconspring.2017.8070099

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Pizzolato et al (2004) used ArcView and the P-median problem to support school locationallocation decisions. Alifi et al (2017) use a newer version of ESRI's GIS software for school network planning and finally, Al-Sabbagh (2020) examined several location-allocation models provided in ArcGIS for school location-allocation planning.…”

Section: Introductionmentioning

confidence: 99%

Spatial Decision Support System for Efficient School Location-Allocation

Batsaris¹,

Kavroudakis²,

Hatjiparaskevas³

et al. 2021

EJG

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In Greece, a lack of a planning strategy was identified in the context of allocating students to schools. Particularly, the Secondary Educational Management of Lesvos Prefecture along with school Principals decide upon student allocation based on empirical knowledge and approximation techniques. As a consequence, during the school season of 2018-2019 capacity and proximity limitations were violated. This study introduces a Spatial Decision Support System (SDSS) to assist school location-allocation decisions in future seasons. The objective of the proposed SDSS is to minimize commute-to-school distance concerning capacity and proximity limitations. For this purpose, a capacitated P-median approach is adopted and formulated as a mixed-integer linear problem. The SDSS is further evaluated using actual data for students' transition from primary to secondary education in the city of Mytilene, Greece. Evaluation of current allocation practices carried out and further compared to those obtained by the SDSS. The results indicate a decrease of 8% in total distance whereas proximity and capacity constraints were respected accordingly. The results may be potentially useful for school planners to assist the allocation decisions in the city of Mytilene.

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Section: Introductionmentioning

confidence: 99%

Spatial Decision Support System for Efficient School Location-Allocation

Batsaris¹,

Kavroudakis²,

Hatjiparaskevas³

et al. 2021

EJG

View full text Add to dashboard Cite

show abstract

“…Students allocation to schools can be viewed as an optimization problem, which object to optimize the number of students who can be served by schools, considering the constraints that apply [2]. In this case, the constraints include the consideration of school and student's residence lie in the same zone, the consideration of number of school demand at each sub-district, and the consideration of students travel distance to school.…”

Section: Introductionmentioning

confidence: 99%

“…Teixeira's study used a mixed integer linear programming approach and solved it using XPRESS-MP solver to optimize the redeployment of secondary school network in Coimbra, Portugal [8]. Alifi's research used ArcGIS Network Analyst tool to build location-allocation optimization model of junior schools in Bandung, Indonesia [2]. Science and Engineering (WCSE 2018) ISBN Although heuristics and integer programming are widely used in facility location research, meta-heuristic approach is also considered as powerful method to solve p-median problems.…”

Section: Introductionmentioning

confidence: 99%

Optimization of School Location-Allocation Using Genetic Algorithm

Prima

Arymurthy²

2018

Proceedings of 2018 the 8th International Workshop on Computer Science and Engineering

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The enactment of zoning policy on Indonesia New Student Admission System requires schools to accept students only from its zone. This condition needs management of school allocation to ensure the facilities suffice the student distribution. Optimization of school location-allocation aims to obtain optimal allocation such that the students travel cost is minimized. This problem can be modelled as p-median problem, and it can be solved using metaheuristic approach. This study utilized genetic algorithm with twodimensional individual representation to solve junior school location-allocation problem in South Jakarta. The results obtained by the proposed algorithm lowers 40.55% of student travel cost compared to the previous allocation with assumption that students tend to choose favorite schools in the center of regency.

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“…Church mentioned the capabilities of GIS such as storage, retrieval, analysis, visualization, and mapping the geospatial data and stated that it is hard to believe that GIS would play just a supporting role in location science in the future. After almost two decades, GIS perhaps still plays a supporting role in location-allocation literature and only few studies have employed tools embedded within a GIS to solve location-allocation optimization models (41)(42)(43).…”

mentioning

confidence: 99%

Where to place emergency ambulance vehicles? Using a capacitated maximum covering location model with real call data

Hashtarkhani

Yin

Kiani

et al. 2021

Preprint

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Background: Timeliness of emergency medical services (EMS) is critical for patient survival. Identifying optimal locations for ambulance vehicles could increase the chance of timely service delivery. This study incorporates Geographical Information Systems (GIS) with a mathematical optimization technique to improve the 5-minute coverage of EMS demands. Methods: This study was conducted in the county of Mashhad, the northeast of Iran, including 94 ambulance vehicles distributed across 74 EMS stations. Locations of demands were extracted using analysis of one-year EMS call data. Network analysis was employed to estimate the travel times. A maximal covering location problem (MCLP) model with a capacity threshold for vehicles was implemented using the CPLEX optimizer. To make the proposed model more practical in the context of EMS, we added a constraint to the model formulation to maintain an acceptable service level for all EMS calls. Two scenarios were implemented: (1) a relocation model of existing vehicles among existing stations and (2) an optimal allocation model of EMS vehicles and stations using a list of candidate locations.Results: Using the relocation model, the proportion of calls for service within the 5-minute coverage standard increased from 69% to 75%, ensuring all urban and rural service demands to be reached within 16 and 48 minutes, respectively. Our allocation model revealed that the coverage proportion could rise to 84% of the total call for service by adding ten vehicles and eight new stations.Conclusions: Incorporating GIS techniques with optimization modeling has the potential to improve population health outcomes in real-world decision making regarding the accessibility and equity of health resource allocation including EMS services.

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Optimization of school network using location-allocation analysis: Case study: Bandung, Indonesia

Cited by 6 publications

References 6 publications

Spatial Decision Support System for Efficient School Location-Allocation

Spatial Decision Support System for Efficient School Location-Allocation

Optimization of School Location-Allocation Using Genetic Algorithm

Where to place emergency ambulance vehicles? Using a capacitated maximum covering location model with real call data

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